This looks similar to some of the billing tools from YC, are you guys doing anything extra?

Curious to hear how others have handled scale challenges in billing infrastructure:

If you're running usage-based billing for AI, infra, or API-heavy platforms— How do you deal with high-throughput event ingestion (say, 10k+ events/sec) without dropping events or messing up customer metering?

We’ve seen setups struggle hard with:

Event ordering guarantees

Idempotency at scale

Handling retries without double-counting

Would love to hear what infra patterns, queues, or storage choices worked (or failed) for you—especially?

Hi HN, Nikhil here, CTO at Flexprice.

To add some context to our journey: before coming to HN, we launched on Product Hunt and spoke with over 100 companies to understand how they approach billing for AI and API-driven products. We’ve seen two main approaches:

1. *Homegrown billing stacks:* Many teams build their own systems in-house, but these quickly become a headache—especially as more products move to pure usage-based pricing or hybrid models with fixed fees. These systems are complex, require significant engineering resources to maintain, and ensuring accuracy is a constant challenge.

2. *Building on top of Stripe/Chargebee:* While these platforms are evolving to support usage-based billing alongside subscriptions, they still lack robust workflows for credits—which are becoming the new currency in usage-based models.

With Flexprice, our goal is to make it easy for developers to get started, integrate quickly, and address the less obvious pain points of usage metering and credits management. We’re focused on supporting all the pricing combinations we see emerging among AI-first companies, and on building an eventually consistent engine that can handle the realities of real-world usage data.

AI pricing models are evolving fast, and we’re eager to hear feedback from the HN community on where the pain points really are, so we can target them directly.

Impressive, Koshima! Billing at scale ,especially with high-frequency event ingestion and credits—is notoriously tricky, so it's great to see an open-source approach to solving these pain points. The real-time debugger and usage analytics API sound particularly useful. Curious how you handle deduplication and idempotency under heavy concurrency. Looking forward to trying this out!

We've been building a GenAI application with fairly simple credit-based workflows to begin with. But just a month into launch, the workflows have started getting more complex, and we’re now rolling out 4 different pricing plans with volume-based tiers.

I was exploring how best to build a flexible credit billing system when I came across Flexprice — and I have to say, it's a great product.

Good to see you here on HN. Wishing you all the success!

Cheers.

Great work! The time-bucketed usage summaries and support for hybrid pricing models are super compelling. How are you handling billing-grade precision for things like token-based metering—especially when upstream services (like OpenAI) may have delays or partial responses? Also curious how you model retroactive adjustments without compromising invoice integrity.

I run my startup for fine tuning llm models for enterprises and providing them inferencing trainmyllm.ai, would love to do a POC with you guys, would solve all my billing related problems with this. Cheers

being on the developer side, the realtime dashboard for admin ui is really useful for end to end testing

so many of the eventually consistent stores reflect so late that its a pain to work with their SDKs

out of curiosity, what kind of metrics or observability do we get for events that got dropped due to some issue